Awesome

IEEE ICCV-2023

Monte Carlo Linear Clustering with Single-Point Supervision is Enough for Infrared Small Target Detection, Boyang Li, Chao Xiao, Longguang Wang, and Yingqian Wang [Paper]

Algorithm Introduction

We propose a Monte Carlo linear clustering (MCLC) method to acheive high-performance infrared small target segmentation with only single point annotation. Experimental results on several datasets (e.g., NUAA, NUDT, IRSTD-1K, and NUDT-Sea) verify the effectiveness of our method. The contribution of this paper are as follows:

1. We propose the first single point supervised method to achieve SIRST detection.

2. Inspired by the inherent characteristic of SIRST, a simple yet effective linear clustering approach with random noise-guided Monte Carlo regularization is proposed to coarsely extract and further refine the candidate target region.

3. Ablation study reveals that pixel-level labels are not necessary for SIRST detection while single-point supervision is strong enough.

Citation

If you find the code useful, please consider citing our paper using the following BibTeX entry.

@inproceedings{li2023monte,
  title={Monte Carlo linear clustering with single-point supervision is enough for infrared small target detection},
  author={Li, Boyang and Wang, Yingqian and Wang, Longguang and Zhang, Fei and Liu, Ting and Lin, Zaiping and An, Wei and Guo, Yulan},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={1009--1019},
  year={2023}
}

Prerequisite

• Tested on Ubuntu 16.04, with Python 3.7, PyTorch 1.7, Torchvision 0.8.1, CUDA 11.1, and 1x NVIDIA 3090 and also

• Tested on Windows 10 , with Python 3.6, PyTorch 1.1, Torchvision 0.3.0, CUDA 10.0, and 1x NVIDIA 1080Ti.

• The NUDT-SIRST download dir (Extraction Code: nudt)

• The NUAA-SIRST download dir [ACM]

• The NUST-SIRST download dir [MDvsFA]

Usage

1. Preprocessing.

Replace the example image in ../dataset/final_dataset_final/NUAA-SIRST/images with the original full images.

Replace the example mask in ../dataset/final_dataset_final/NUAA-SIRST/masks with the original full masks.

cd final_size_Prior_code
python generate_single_point_Prior.py

2. Pseudo Label Generation.

cd final_size_Prior_code
python Baseline_NUAA.py

3. Retrain SIRST Detection Network (e.g, DNANet) with the Generated Label.

SIRST Label Geneartion Software

Based on the algorithm in the above paper, we developed a single-point annotation software. Download dir